Radiation Risks and Mitigation in Electronic Systems

Electrical and electronic systems can be disturbed by radiation-induced effects. In some cases, radiation-induced effects are of a low probability and can be ignored; however, radiation effects must be considered when designing systems that have a high mean time to failure requirement, an impact on protection, and/or higher exposure to radiation. High-energy physics power systems suffer from a combination of these effects: a high mean time to failure is required, failure can impact on protection, and the proximity of systems to accelerators increases the likelihood of radiation-induced events. This paper presents the principal radiation-induced effects, and radiation environments typical to high-energy physics. It outlines a procedure for designing and validating radiation-tolerant systems using commercial off-the-shelf components. The paper ends with a worked example of radiation-tolerant power converter controls that are being developed for the Large Hadron Collider and High Luminosity-Large Hadron Collider at CERN.Comment: 19 pages, contribution to the 2014 CAS - CERN Accelerator School: Power Converters, Baden, Switzerland, 7-14 May 201

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Review of aerodynamic design in the Netherlands

Aerodynamic design activities in the Netherlands, which take place mainly at Fokker, the National Aerospace Laboratory (NLR), and Delft University of Technology (TUD), are discussed. The survey concentrates on the development of the Fokker 100 wing, glider design at TUD, and research at NLR in the field of aerodynamic design. Results are shown to illustrate these activities

A model-based reasoning architecture for system-level fault diagnosis

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.Ph.D.Committee Chair: Vachtsevanos, George; Committee Member: Liang, Steven; Committee Member: Michaels, Thomas; Committee Member: Vela, Patricio; Committee Member: Wardi, Yora

Best-fit quasi-equilibrium ensembles: a general approach to statistical closure of underresolved Hamiltonian dynamics

A new method of deriving reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a set of resolved variables that define a model reduction, the quasi-equilibrium ensembles associated with the resolved variables are employed as a family of trial probability densities on phase space. The residual that results from submitting these trial densities to the Liouville equation is quantified by an ensemble-averaged cost function related to the information loss rate of the reduction. From an initial nonequilibrium state, the statistical state of the system at any later time is estimated by minimizing the time integral of the cost function over paths of trial densities. Statistical closure of the underresolved dynamics is obtained at the level of the value function, which equals the optimal cost of reduction with respect to the resolved variables, and the evolution of the estimated statistical state is deduced from the Hamilton-Jacobi equation satisfied by the value function. In the near-equilibrium regime, or under a local quadratic approximation in the far-from-equilibrium regime, this best-fit closure is governed by a differential equation for the estimated state vector coupled to a Riccati differential equation for the Hessian matrix of the value function. Since memory effects are not explicitly included in the trial densities, a single adjustable parameter is introduced into the cost function to capture a time-scale ratio between resolved and unresolved motions. Apart from this parameter, the closed equations for the resolved variables are completely determined by the underlying deterministic dynamics

ID defect minimization in glass substrate industry

This study is mainly about the ID defect reduction project in glass substrate manufacturing industry. By using quality tools, lean manufacturing approach, and total quality management, defect can be reduce by controlling on 4 variables: data variance in SPC, measurement tools accuracy, machine stability and operator skills. Based on these four variables, operator skills have higher tendency on defect generation in glass substrate manufacturing if compared to others. Thus, company should focus on the enhancement activity to lift up operator skills and motivation in order to reduce the defect rate